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Machine Learning, Statistical Analysis, Python, R, SQL

Location:
Bloomington, IN
Posted:
February 21, 2020

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Resume:

Aditya Kartikeya Mallajosyula

adbwf3@r.postjobfree.com +1-470-***-**** Bloomington, Indiana

linkedin.com/in/aditya-kartikeya-a37651164/ github.com/kartik2207?tab=repositories EDUCATIONAL QUALIFICATIONS:

Indiana University at Bloomington, Luddy School of Informatics, Computing and Engineering May 2021 Master of Science in Data Science GPA:

3.8/4.0

Ongoing Coursework: Artificial Intelligence, Machine Learning, Statistics, Time series analysis, Access and Management of Big data.

National Institute of Technology, Warangal May 2019 Bachelor of Technology in Electronics and Communications Engineering GPA: 3.6/4.0 SPDC Scholarship awardee for four years of bachelor's degree. Relevant Coursework: Problem Solving and Computer programming, Data Structures and Algorithms Online Courses: Deep learning Specialization by Andrew Ng Coursera, Machine Learning by Andrew Ng Coursera TECHNICAL SKILLS:

Programming/Scripting Languages: Python, C++, SQL, R Machine Learning: Regression Techniques,Exploratory Data Analysis, KNN, Naive Bayes, SVM, PCA, T-SNE, Tree-Based Models(Decision Trees), Neural Networks(CNN),Bayes Nets, HMM, Gradient boosting, MCMC, Time Series, Feature Engineering, Generalized linear models(GLM).

Statistics: Hypothesis Testing, T-Test, Z Test, Gradient descent, Newton’s Method, ANOVA test, Chi-square test. Libraries: Numpy, Pandas, Matplot-lib, Scikit-learn, NLTK, plotly, seaborn, Open CV Tools and Platforms: MS Excel, MS Office, Powerpoint, Tensorflow, Keras, MySQL, Hadoop, Tableau. WORK EXPERIENCE: Associate Instructor January 2020 - Present

Luddy School of Informatics, Computing and Engineering, IU, Bloomington, Indiana

● Design the assignments for undergraduate class of Principles of Machine Learning and conduct office hours to clear doubts.

● Mentor groups of students to develop machine learning projects and assignments that help them gain experience that is valuable in the industry.

● Helped the students on how to use python, and several libraries such as pandas, numpy and also taught them how to use Google Collab and Jupyter notebook

ZexKart January 2018-November 2018

Co-Founder Search engine optimization, e-commerce, Google Ads, Facebook Ads.

● Designed a fully functional e-commerce website from scratch and performed Search engine optimization to increase the traffic to the website. Facilitated worldwide shipping of merchandise and built a network with chinese suppliers. Aimed at giving Best Value and satisfaction to the customers.

● Well versed with Google Ads, Facebook Ads and Instagram Ads system. Used them to promote our company across the globe. PROJECTS:

Microsoft Malware Detection- Kaggle Question Python, Dask, Numpy November 2019

● Predicted a Windows machine’s probability of getting infected by various families of malware, based on different properties of that machine. The telemetry data containing these properties and the machine infections was generated by combining heartbeat and threat reports collected by Microsoft’s endpoint protection solution, Windows Defender.

● The most successful Model capable of high performance was a tree based model. Image orientation classifier Python, numpy November 2019

● Built 3 models from scratch using Python and numpy to classify the orientation of an image in the dataset. Decision tree model performed with 64% accuracy, K-NN performed with 72% accuracy and Neural Network performed with 87% accuracy. New York City Taxi Demand Prediction- Kaggle Python,Sklearn, Pandas, Google Cloud Platform, Dask October 2019

● Predicted the number of pickups in that given location at a particular time interval(in a 10 min interval)as accurately as possible in New York City where the end user of the prediction is a Taxi Driver.

● Compared multiple models(Linear Regressor, Random Forest Regressor, XGBoost Regressor) to get the best accuracy, and found out that XGBoost Regressor is the best model with the best accuracy. Classification of Projects on Donors Choose Dataset Sklearn, Numpy, Pandas June 2019

● Built a model for the Donors choose Dataset and predicted whether a proposed project would receive funding.

● Cleaned the dataset and performed Exploratory data analysis on the dataset

● Used dimensionality reduction techniques (PCA and t-SNE) and built 2 different models(Logistic Regression, Naive Bayes,) and compared the performances.

LEADERSHIP EXPERIENCE

● Co-Founder and General Secretary of FINWIZ: A Society to improve Stock market and investment awareness among budding engineers.

● U-19 National Team Player: Played cricket for Qatar national cricket team.

● Team Captain:University cricket team captain (NIT Warangal) 2018-2019

● Active day trader(technical analyst) on NSE.



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